Biodiesel is viewed as the alternative fuel to the petroleum diesel due to the renewable and environmental friendly properties. Biodiesel is a mixture of fatty acid methyl esters (FAME) produced from vegetable oils/animal fats by transesterification with methanol as well as other constituents. The compositions of FAME are constrained by the feedstock of vegetable oils/animal fats. There are six main types of FAME in biodiesel: methyl palmitate (C16:0), methyl palmitoleate (C16:1), methyl stearate (C18:0), methyl oleate (C18:1), methyl linoleate (C18:2) and methyl linolenate (C18:3); however, there may be other components known to a person having ordinary skill in the art.
The compositions of the FAME significantly affect the cold flow properties. Cold flow properties are the performances of biodiesel at low temperature. Cold flow properties of FAME can be characterized by cloud point, pour point, cold filter plugging point, and low temperature filterability test. Moreover, in North America, cloud point is used as the most appropriate standard to characterize the cold flow properties of FAME. Cloud point is referred as the temperature when biodiesel starts to form crystals (when phase separation begins to appear (i.e., when the mixture becomes “cloudy”) and the thickening fluid can clog filters or other orifices). According to the definition of cloud point, cloud point show FAME change from pure liquid mixture to liquid/solid mixtures. Therefore, cloud point is a phenomenon of solid-liquid equilibrium. The cloud point of FAME depends on the composition because the main FAME components have different melting points (as shown in Table 1). The mixture of FAME with high level of high melting point components will result in a high cloud point.
TABLE 1Melting point of substantially pure FAME componentsComponentsMelting point (° C.)C16:030C16:10.5C18:038C18:1−20C18:2−35C18:3−52
The quantitative relationship between the composition of FAME and cloud point is known. For example, Liu et al. have established the quantitative relationship between the composition of FAME and the cloud point through multiple linear statistical regression. This quantitative model shows fatty acid methyl esters with high melting points have more significant effect than those with low melting points. However, the prediction model is challenged due to a low value of R2 (proportion of variability in a data set based on how well future outcomes are predicted by a model). Imahara et al. use the thermodynamic phase heterogeneous equilibrium principal to predict the cloud point of fatty acid methyl esters according to the fraction of high melting point component. This prediction model is also challenged because the interaction between the components is not considered. Boros et al. used the thermodynamic model to predict the cloud point of fatty acid methyl esters with the UNIQUAC (UNIversal QUAsiChemical is an activity coefficient model used in description of phase equilibria) to predict the non-ideal behavior and as a result the predictability of the model significantly improved. However, their model is also challenged since it needs to be provided various parameters when a new component is added into a mixture.
While UNIFAC (UNIversal Functional Activity Coefficient) models (see Zhong, Sato, Masuoka, and Chen) have been used for predicting liquid-vapor transitions, the UNIFAC model or the modified UNIFAC model (see Gmehling, Li, and Schiller; Lohmann & Gmehling; Lohmann, Röpke, and Gmehling; Weidlich and Gmehling; and Wittig, Lohmann, and Gmehling) has not been used for predicting liquid-solid transition.
A basic challenge, therefore, remains. Specifically, when various components of fatty acid methyl esters from different sources are added, predicting the cloud point of the new mixture remains a challenge. This challenge is especially problematic since fatty acid methyl esters can originate from many sources. In fact the number of sources from which FAME can originate from may be more diverse than sources of fossil fuel. Furthermore, there can be various additives that can be included in the overall composition. Each of these presents a significant challenge for predicting the cloud point of the mixture.
Therefore, in light of the foregoing challenges with cloud point prediction, a method and a system for accurately predicting cloud point in a mixture of fatty acid methyl esters is needed where the method utilizes molecular interactions between the esters and the relationship therebetween to further provide accuracy to the prediction.